Expression Invariant Face Recognition System based on Topographic Independent Component Analysis and Inner Product Classifier
Research Paper | Journal Paper
Vol.5 , Issue.12 , pp.1-6, Dec-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i12.16
Abstract
A technique for expression invariant face recognition using topographic modelling approach for feature extraction and Inner Product Classifier for performing classification of the faces is proposed. The topographic analysis which treats the image as a 3D surface and labels each pixel by its terrain features is used as the base for feature extraction. Based on this concept, the Topographic Independent Component Analysis (TICA) has been used to obtain the independent components such that the dependence of two components is approximated by their proximity in the topographic representation. The components that are not close to each other in the topography are independent. TICA is an extension of Independent Component Analysis for which a model needs to be developed that represents the correlation of energies for components that are close in the topographic grid. This methodology was used to extract such features from the face that are independent in terms of topography and thus invariant to changes in expression to a large extent. The feature vectors thus generated were input to the Inner Product Classifier (IPC) which considers the errors between the training and the test image features bases on triangular or t-norms. Triangular norms highlight the errors and determine a margin between them. Inner product between the aggregated training features vector and t-norm of the error vectors should be the least for the test feature vectors so as to match with the training feature vectors. The training feature vectors with the least inner product or margin give the identity of the test feature vector. Application of an effective feature extraction technique based on topographically independent components, and its combination to a classifier that works on the principle of minimization of error between the features by emphasising a margin between them, yields an efficient design for an expression invariant face recognition system.
Key-Words / Index Term
Topographic Independent Component Analysis, Terrain Features, Correlation of Energies, Frank t-norm, Inner Product Classifier
References
[1]. M.Turk & A.Pentland, (1991) “Eigenfaces for Recognition”, Journal of Cognitive Neuroscience, vol.3, no.1, pp. 71-86, 1991a.
[2]. M. Turk & A. Pentland, (1991) “Face Recognition Using Eigenfaces”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586-591.
[3]. Rothkrantz M., “Automatic Analysis of Facial Expressions: The State of the Art,” IEEE Transaction Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1424-1445, 2000.
[4]. Fasel B. and Luettin J., “Automatic Facial Expression Analysis: a Survey,” IEEE Pattern Recognition, vol. 36, no. 1, pp. 259-275, 2003.
[5]. E. Mary Shyla , Dr.M.Punithavalli, "Hybrid Facial Color Component Feature Identification Using Bayesian Classifier", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.3, pp.14-21, 2013.
[6]. Ang L., Belen E., Bernardo R., Boongaling E., Briones G., and Corone J., “Facial Expression Recognition through Pattern Analysis of Facial Muscle Movements Utilizing Electromyogram Sensors,” in Proceedings of IEEE TENCON, vol. 3, pp. 600-603, 2004.
[7]. A. Sur , S. Sarkar , K. Sarkar , "An Approach towards Face Counting System using Image Processing Techniques", International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.34-37, 2017.
[8]. Pentland A., Moghaddam B., and Starner T., “View-Based and Modular Eigenspaces for Face Recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.84-91, 1994.
[9]. I. Essa and A.P.Pentland. Coding, analysis, interpretation and recognition of facial expressions. Pattern Analysis and Machine Intelligence, IEEE Transactions, 19(7):757–763, Jul 1997.
[10]. Zhang Y. and Martinez A., “Recognition of Expression Variant Faces Using Weighted Subspaces,” in Proceedings of the 17th International Conference on Pattern Recognition, vol. 3, pp. 149-152, 2004.
[11]. Vretos N., Nikolaidis N., and Pitas I., “A Model-Based Facial Expression Recognition Algorithm using Principal Component Analysis,” in Proceedings of the 16th IEEE International Conference on image Processing, Cairo, pp.3301-3304, 2009.
[12]. Kuilenburg H., Wiering M., and Uyl M., “A Model Based Method for Automatic Facial Expression Recognition,” Springer Verlag Proceedings of the ECML, vol. 54, pp. 194-205, 2005.
[13]. Kotsia I. and Pitas I., “Facial Expression Recognition in Image Sequences using Geometric Deformation Features and Support Vector Machines” IEEE Transaction on Image Processing, vol. 16, no. 1, pp. 172-187, 2007.
[14]. Jun C., liang W., Guang X., and Jiang X., “Facial Expression Recognition based on Wavelet Energy Distribution features and Neural Network Ensemble,” in Proceedings of Global Congress on Intelligent Systems, Xiamen, vol. 2, pp. 122-126, 2009.
[15]. Bettadapura V., “Face Expression Recognition and Analysis: The State of the Art,” in Proceedings of IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 22, pp.1424-1445, 2002.
[16]. Bartlett M., Littlewort G., Lainscsek C., Fasel I., and Movellan J., “Machine Learning Methods for Fully Automatic Recognition of Facial Expressions and Facial Actions,” in Proceedings of IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 592-597, 2004.
[17]. L. Wang, T. Pavlidis, Direct gray-scale extraction of features for character recognition, IEEE Trans. PAMI 15 (1993) 1053–1067.
[18]. O. Trier, T. Taxt, A. Jain, Ata capture from maps based on gray scale topographic analysis, in: The Third International Conference on Document Analysis and Recognition, Montreal, Canada, 1995.
[19]. Aapo Hyvarinen, Patrik O. Hoyer, and Mika Inki, "Topographic Independent Component Analysis", Neural Computation 13(7):1527-1558 (July, 2001).
[20]. Jutten, C. and Herault, J. (1991). Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24:1–10
[21]. Mamta, Madasu Hanmandlu, "Robust ear based authentication using Local Principal Independent Components", Expert Systems with Applications 40 (2013) 6478–6490 (Elsevier).
[22]. Michael J. Lyons, Shigeru Akamatsu, Miyuki Kamachi & Jiro Gyoba JAFFE database, Coding Facial Expressions with Gabor Wavelets Third IEEE International Conference on Automatic Face and Gesture Recognition, April 14-16 1998, Nara Japan, IEEE Computer Society, pp. 200-205.
[23]. T. Kanade, J. Cohn, Y. Tian, “Comprehensive database for facial expression analysis”, in: IEEE 4th International Conference on FGR, France, 2000.
Citation
Aruna Bhat, "Expression Invariant Face Recognition System based on Topographic Independent Component Analysis and Inner Product Classifier," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.1-6, 2017.
Using Reference Point-Based NSGA-II to System Reliability
Research Paper | Journal Paper
Vol.5 , Issue.12 , pp.7-14, Dec-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i12.714
Abstract
In principle, a multi-objective optimization problem (MOOP) provides a group of non-dominated solutions (popularly known as Pareto-optimal solutions) for the decision maker (DM). A DM is undecidable to claim one of these solutions to be better than another in the absence of any further information. Due to this reason, a DM needs as many Pareto-optimal solutions as possible. Classical optimization methods are unable to produce multiple solutions at a time because of converting the MOOP to a single-objective optimization problem (SOOP). In the past decades, multi-objective evolutionary algorithms (MOEAs) have been developed to be powerful techniques of identifying a complete picture of the Pareto-optimal solutions space, where a DM can select one out of these solutions according to his/her preference. Moreover, a more efficient MOEA can exploit the search in a better position if the DM provides some general views or ideas about the solution in terms of reference points or weights. Reference point based NSGA-II (R-NSGA-II) is such type of an MOEA where DM’s assigned reference points are used to search the solutions and its diversity is controlled efficiently. This paper presents the applicability of the R-NSGA-II algorithm to the system reliability design problem. The simulation results show the advantage of R-NSGA-II over NSGA-II.
Key-Words / Index Term
Multi-objective optimization problem (MOOP), Multi-objective evolutionary algorithms (MOEAs), Reference points, System reliability, Pareto-optimal front (POF)
References
[1] K. Deb, “Multi-objective optimization using evolutionary algorithms”, John Wiley & Sons, 2001.
[2] J. Knowles, D. Corne, “The Pareto archived evolution strategy: A new baseline algorithm for multiobjective optimization”, In Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, DOI: 10.1109/CEC.1999.781913, 1999.
[3] N. Srinivas, K. Deb, “Multi-objective optimization using non-dominated sorting in genetic algorithms”, Evol. Comput., Vol. 2, no. 3, pp. 221-248, 1994.
[4] J. Horn, N. Nafploitis, D. Goldberg, “A niched Pareto genetic algorithm for multi-objective optimization”, In Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 82-87, 1994.
[5] E. Zitzler, L. Thiele, “An evolutionary algorithm for multi-objective optimization: The strength Pareto approach”, Technical report 43, Zurich, Switzerland: Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), 1998.
[6] K. Deb, S. Agarwal, A. Pratap, T. Meyarivan, “A fast and elitist multi-objective genetic algorithm: NSGA-II”, IEEE Trans. Evol. Comput., Vol. 6, pp. 182-197, 2002.
[7] K. Deb, J. Sundar, U.B. Rao, S. Chaudhuri, “Reference Point Based Multi-Objective Optimization Using Evolutionary Algorithms”, International Journal of Computational Intelligence Research, Vol. 2, No. 3, pp. 273-286, 2006.
[8] H. Garg, S.P. Sharma, “Multi-objective reliability-redundancy allocation problem using particle swarm optimization”, Computers & Industrial Engineering, Vol. 64, No. 1, pp. 247-255, 2012.
[9] G.D. Goldberg, “Genetic algorithms for search, optimization, and machine learning”, Reading, MA: Addison-Wesley, 1989.
[10] D. Salazar, C.M. Rocco, B. J. Galvan, “Optimization of constrained multiple objective reliability problems using evolutionary algorithms”, Reliability Engineering and System Safety, 91, pp. 1057-1070, 2006.
[11] A. Kishore, S. P. Yadav, S. Kumar, “Application of a Multi-objective Genetic Algorithm to solve Reliability Optimization Problem”, International Conference on Computational Intelligence and Multimedia Applications, pp. 458-462, DOI: 10.1109/ICCIMA, 2007.
[12] A. Kishore, S. P. Yadav, S. Kumar, “Interactive fuzzy multiobjective optimization using NSGA-II”, OPSEARCH, Vol. 46, No. 2, pp. 214-224, 2009.
[13] Z. Wang, T. Chen, K. Tang., X. Yao, “A Multi-objective Approach to Redundancy Allocation Problem in Parallel-series Systems”, IEEE, pp. 582-589, DOI: 978-1-4244-2959-2/09, 2009.
[14] J. Safari, “Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies”, Reliab Eng Syst Saf., 108, pp. 10–20, 2012.
[15] K. Khalili-Damghani, A. R. Abtahi, M. Tavana, “A decision support system for solving multi-objective redundancy allocation problems”, Qual Reliab Eng Int, Vol. 30, No. 8, pp. 1249-1262, 2014.
[16] A. Taboada, F. Baheranwala, D.W. Coit, “Practical solutions for multi-objective optimization: An application to system reliability design problems”, Rel. Engg. Syst. Saft., 92, pp. 314-322, 2007.
[17] K.K. Aggarwal, J.S. Gupta, “On minimizing the cost of reliable systems”, IEEE Transaction on Reliability R-24 (3), pp. 205, 1975.
[18] V. Ravi, B.S.N. Murthy, P.J. Reddy, “Nonequilibrium simulated annealing algorithm applied to reliability optimization of complex systems”, IEEE Trans. On Rel., Vol. 46, No. 2, pp. 233-239, 2000.
Citation
H. Kumar, S.P. Yadav, "Using Reference Point-Based NSGA-II to System Reliability," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.7-14, 2017.
A Point of Two Mode-Session Logs Based Web User Interest Prediction System From Web Search Engine
Research Paper | Journal Paper
Vol.5 , Issue.12 , pp.15-22, Dec-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i12.1522
Abstract
Information prediction from web search is a hands-off process based on user interest. By differently user may think based on the knowledge or relevant search things, but web mining tasks are complex to understanding the user behaviour and knowledge to retrieve the right things to the user. To propose a new intent interest prediction for improving the efficiency, we propose a session timing preference based two mode user interest prediction methods called semantic log pre-fetch clustering (SLCPC) algorithm and personalized feed ranking (FPR) algorithm to deduce a set of related categories for each user query based on the retrieval history of the user session, i.e. different contents have to be placed for different users according to the user relevant search profiles spending time on interested logs. Two modes analyse behaviours like time of visit, navigation URL, weblogs, and user actions on the webpage. SLPC predicts web log data of the user and also identifies the implicit behaviours performed by the user. The identified information is used to identify the user interest and seeds are generated by logging page. So the user doesn`t wait for long more time to search our interesting factor frequently. The web users are clusters based on the feed ranking interest which is used continues searches. Our proposed system improves the accuracy of personalized user search compared to an existing approach.
Key-Words / Index Term
Personalization, Prediction mining, Web search, Clustering, Session logs
References
[1] Kenneth Wai-Ting Leung and Dik Lun Lee. ”Deriving Concept-Based User Profiles from Search Engine Logs,” IEEE Trans. Knowledge and Data Eng., vol. 22, Issue. 7,pp.969 – 982,2010.
[2] K.W.-T. Leung, W. Ng, and D.L. Lee, “Personalized Concept-Based Clustering of Search Engine Queries,” IEEE Trans. Knowledge and Data Eng., vol. 20, Issue. 11, pp.1505-1518,2008.
[3] M.speretta and S.Gauch, “Personalized Search Based on User Search Histories,” IEEE Trans.Web Intelligence, vol 10,Issue 7,pp.192.212,2005.
[4] R.Baeze-yates, C.Hurtado, and M.Mendoza, “Query Recommendation Using Query Logs in Search Engines,” Proc.Int’l Workshop Current Trends in Database Technology, pp.588-596, 2004.
[5] Y.Xu,K. Wang, B.Zhang, and Z.Chen, “Privacy-Enhancing Personalized Web search,” Proc. World Wide Web(WWW) Conf., 2007.
[6] Zheng Lu,By, HongyuanZha, Xiaokang Yang, Weiyao Lin, And ZhaohuiZheng “A New Algorithm For Inferring User Search Goals With Feedback Sessions” , IEEE Trans. Knowledge and Data Eng., vol. 25, Issue. 3, pp.502-513,2013.
[7] Thanh Sang Nguyen, Hai Yan Lu, and Jie Lu. “Web-Page Recommendation Based on Web Usage and Domain Knowledge”, IEEE Trans. Knowledge and Data Eng., vol. 26, Issue. 10, pp.2574-2587, 2014.
[8] AthanasiosPapagelis and Christos Zaroliagis “A Collaborative Decentralized Approach to Web Search“,IEEE Transactions on Systems, Man, and Cybernetics ,vol 45,issue 5,pp 1271- 1290, 2012.
[9] Dimitriosierrakos and George Palioura, “Personalizing Web Directories with the Aid of Web Usage Data” IEEE Trans. Knowledge and Data Eng., vol. 22, Issue. 9.pp 1331-1343, 2010.
[10] JunyuXuan, XiangfengLuo, Guangquan Zhang, Jie Lu, and ZhengXupp “Uncertainty Analysis for the Keyword System of Web Events, IEEE Trans. Knowledge and Data Eng., vol. 42, Issue. 10 ,829-842,2016.
[11] Rakish Kumar and AditiSharan, “Personalized web search using browsing history and domain knowledge”, IEEE Trans. Intelligent computing, vol. 12, Issue. 2 pp. 2161-2174, 2014.
[12] Anoj Kumar and Mohd. Ashraf, ”Efficient Technique for personalized web search using users browsing history”, IEEE Trans. Intelligent computing ,vol 3, Issue 4. 2015 .
[13] Y.Mao, H Shen, “Web of Credit: Adaptive Personalized Trust Network Inference From Online Rating Data”, IEEE Trans. Network and Intelligent computing ,vol 2, Issue 4.pp.234-2452, 2015.
[14] KamleshMakvana, Pinal Shah and Shah, “A Novel Approach to Personalize Web Search through User Profiling and Query Reformulation”, IEEE Trans. Knowledge and Data Eng., vol. 26, Issue. 7.pp 1121-1139 2014.
[15] Jayaraj Jayabharathy , Selvadurai Kanmani “ Correlated concept based dynamic document clustering algorithms for newsgroups and scientific literature “ springer decision analytics, 2014.
[16] R. Hu, W. Dou, X. F. Liu, and J. Liu, “Personalized searching for web service using user interests,” in Dependable, Autonomic and Secure Computing (DASC),2011 IEEE Ninth International Conference on. IEEE,India, 2011.
[17] Enrico Sartori, Yannis Velegrakis, and Francesco Guerra,” Entity-Based Keyword Search in Web Documents”, Proceeding Transactions on Computational Collective Intelligence, Springer, pp. 21–49, 2016.
[18] H.-j. Kim, S. Lee, B. Lee, and S. Kang, “Building concept network-based user profile for personalized web search,” in Computer and Information Science (ICIS), IEEE/ACIS 9th International Conference on. IEEE,India, pp. 567–572. 2010
[19] EhsanElhamifar, and Rene Vidal, “Sparse Subspace Clustering: Algorithm, Theory, and Applications”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, Issue. 11, pp. 2765-2781, 2013.
[20] Caimei Lu, Xiaohua Hu and Jung-ran Park, “Exploiting the Social Tagging Network for Web Clustering”, IEEETransaction on Systems, Man and Cybernetics, vol.41, Issue. 5, pp. 840-852, 2011.
Citation
R. Velmurugan, S.P. Victor, "A Point of Two Mode-Session Logs Based Web User Interest Prediction System From Web Search Engine," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.15-22, 2017.
Performance Analysis of Turbo Codes Using CRC and FC with OFDM
Research Paper | Journal Paper
Vol.5 , Issue.12 , pp.23-28, Dec-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i12.2328
Abstract
Long Term Evolution (LTE) is an evolving wireless communication standard that requires high data rate, higher bandwidth efficiency and better coverage. So, spectrally efficient techniques like Orthogonal Frequency Division Multiplexing (OFDM), Multiple Input Multiple Output and Error Control Codes with high coding gain are constituents of LTE. OFDM partitions a channel with wide bandwidth into several channels with narrow bandwidth that are flat, in order to mitigate the effects of multipath fading and Inter Symbol Interference (ISI). Error control codes enhance reliable transmission over noisy fading channels. The objective of this work is to increase data rate and reduce bit error rate by combining OFDM and Turbo Codes. Bit error rate is further reduced by applying Cyclic Redundancy Check (CRC) and Flip and Check algorithm (FC) along with turbo codes. In this paper, MAP and MAX Log MAP algorithms have been applied in the turbo decoding process and their performance compared. The proposed algorithm has been implemented and simulated using MATLAB.
Key-Words / Index Term
Turbo Codes, MAP, MAX Log MAP, Cyclic Redundancy Check, Flip and Check, OFDM, LTE
References
[1] A. C. Reid, T. A. Gulliver, D. P. Taylor, "Convergence and Errors in Turbo-decoding", IEEE Transactions on Communications, Vol.49, Issue 12, pp. 2045-2051, 2001.
[2] S C. Berrou, A. Glavieux, P. Thitimajshima, “Near Shannon Limit Error - Correcting Coding and Decoding : Turbo-codes 1”, Proceedings of the IEEE International Conference on Communication, Geneva, pp.1064 – 1070, 1993.
[3] L. A. Perişoară, R. Stoian ,“The Decision Reliability of MAP, Log-MAP, Max-Log-MAP and SOVA Algorithms for Turbo Codes”, International Journal of Communications, Vol. 2, Issue 1, pp. 65-74, 2008.
[4] B. Sklar, “Digital Communications : Fundamentals and Applications”, Prentice Hall, USA, pp. 475 – 510, 2001.
[5] F. Daneshgaran, M. Laddomada, and M. Mondin. "Interleaver design for serially concatenated convolutional codes: theory and application." IEEE Transactions on Information Theory, Vol. 50, Issue 6, pp. 1177-1188, 2004.
[6] F. Daneshgaran, M. Laddomada, "Reduced complexity interleaver growth algorithm for turbo codes ", IEEE Transactions on Wireless Communications, Vol. 4, Issue 3, pp. 954-964, 2005.
[7] J. Yu, M-L. Boucheret, R. Vallet, A. Duverdier, G. Mesnager, "Interleaver Design for Turbo Codes from Convergence Analysis", IEEE Transactions on Communications, Vol. 54, Issue 4, pp. 619-624, 2006.
[8] C. F. Leanderson, C-E.W Sundberg, "On List Sequence Turbo Decoding", IEEE Transactions on Communications, Vo. 53, Issue 5, pp. 760-763, 2005.
[9] K. R. Narayanan, G. L. Stuber. "List Decoding of Turbo Codes", IEEE Transactions on Communications, Vol. 46, Issue 6, pp.754-762, 1998.
[10] T.Tonnellier, C.Leroux, B. Le Gal, B. Gadat, C. Jego, N. V. Wambeke, “Lowering the Error Floor of Turbo Codes With CRC Verification”, IEEE Wireless Communications Letters, Vol. 5, Issue 4, pp. 404 – 407, 2016.
[11] Y. Wu, W. Y. Zou, “Orthogonal frequency division multiplexing : a multi-carrier modulation scheme”, IEEE Transactions on Consumer Electronics, Vol. 41, Issue 3, pp. 392 – 399, 1995.
[12] P. Vyas, N. Parihar, V. Gupta, R. Jain, "A Comparison Study for PSD Performance in OFDM Systems Based on Using Autocorrelation Techniques", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.2, pp.10-14, 2013.
[13] R. Saxena, N. Gupta, N. Praveen, “Use of turbo coding in OFDM system for improving BER; A Review”, International Journal of Engineering and Innovative Technology, Vol. 2, Issue 7, pp. 81 – 84, 2013.
[14] D. G. Agrawal, R. K. Paliwal, P. Subramaniam, “Effect of Turbo Coding on OFDM Transmission to Improve BER,” International Journal of Computer Technology and Electronics Engineering, Vol. 2, Issue 1, pp. 94 – 102, 2012.
[15] R. Singh, “4G By WiMAX2 and LTE-Advance”, International Journal of Computer Sciences and Engineering, Vol. 1, Issue 3, pp. 36-38, 2013.
[16] S. M. Chadchan, C. B. Akki. "3GPP LTE/SAE: An Overview." International Journal of Computer and Electrical Engineering, Vol. 2, Issue 5, pp. 806 – 814, 2010.
[17] C. Anghel, V. Stanciu, C. Paleologu, “CTC Turbo Decoding Architecture for LTE Systems Implemented on FPGA,” Proceedings of the Eleventh International Conference on Networks (ICN 2012), Reunion Island, pp. 199 – 204, 2012.
[18] P. Robertson, E. Villebrun, P. Hoeher, “A Comparison of Optimal and Sub-optimal MAP Decoding Algorithms Operating in the Log Domain”, IEEE International Conference on Communications, USA, pp. 1009-1013, 1995.
[19] J. F. Cheng, H. Koorapaty. "Error detection reliability of LTE CRC coding", Proceedings of the IEEE Vehicular Technology Conference (Fall), Canada, pp. 1 – 5, 2008.
Citation
A.Vasuki, K.Kavitha, G.Sowndharya, "Performance Analysis of Turbo Codes Using CRC and FC with OFDM," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.23-28, 2017.
Evaluation of a NeuroFuzzy Unsupervised Feature Selection Approach
Research Paper | Journal Paper
Vol.5 , Issue.12 , pp.29-34, Dec-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i12.2934
Abstract
Dimensionality reduction is a commonly used step in machine learning, especially when dealing with a high dimensional space of features. The original feature space is mapped onto a new, reduced dimensionality space and the examples to be used by machine learning algorithms are represented in that new space. The mapping is usually performed either by feature extraction or feature selection. Feature extraction involves constructing some new features from original feature set. Feature selection involves selecting a subset of the original features from original feature set without transformation. Feature selection can be implemented either by feature ranking or subset selection. Feature ranking is an approach in which all the features are ranked based on some criteria. In this project, Feature ranking algorithm has been implemented. Work presented here includes the implementation of UFSNF for ranking different features using the fuzzy evaluation index with neural networks. The results (ranks) obtained from UFSNF have been compared with the ranks obtained by Relief-F evaluator using four clustering techniques EM, k-Means, Farthest First and Hierarchical. For the experimental study, benchmark datasets from the UCI Machine Learning Repository have been used. From the study, it is found that the newly proposed algorithm, UFSNF in some cases exceeds the performance of Relief-F.
Key-Words / Index Term
Dimensionality reduction,feature selection,unsupervised, Relief-F,clustering
References
[1] Sankar K. Pal, Fellow, IEEE, Rajat K. De, Member, IEEE, and Jayanta Basak, Senior Member, IEEE “Unsupervised learning: Neuro fuzzy approach”, IEEE Transactions on Neural Networks, vol. 11, no. 2, MARCH 2000.
[2] E. C. C. Tsang, D. S. Yeung, and X. Z. Wang, “Optimal Fuzzy-Valued Feature Subset Selection”, IEEE Transactions On Fuzzy Systems, vol. 11, no. 2, APRIL 2003
[3] Hahn-Ming Lee, Chih-Ming Chen, Jyh-Ming Chen, and Yu-Lu Jou, “An Efficient Fuzzy Classifier with Feature Selection Based on Fuzzy Entropy”, IEEE Transactions on Systems, Man, and Cybernetics—PART B: CYBERNETICS, vol. 31, no. 3, JUNE 2001
[4] http://archive.ics.uci.edu/ml/datasets.html
Citation
Bacharaju Vishnu Swathi, "Evaluation of a NeuroFuzzy Unsupervised Feature Selection Approach," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.29-34, 2017.
Performance Analysis of Real-Time Eye Blink Detector for Varying Lighting Conditions and User Distance from the Camera
Research Paper | Journal Paper
Vol.5 , Issue.12 , pp.35-40, Dec-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i12.3540
Abstract
This paper presents the performance analysis of a blink detector, which detects eye blink, right wink and left wink, under natural & controlled lighting conditions and for variable user distance from the camera. The blink detector has been implemented by using a webcam, a computer and MATLAB software with image processing and computer vision toolbox. It divides the whole process of blink detection into three parts: face and eyes pair localization, blink detection using pixels’ motion analysis and classification of blinks as left wink, right wink and eye blink i.e. blinking both eyes simultaneously. The detection accuracy of the detector was measured under natural and controlled lighting conditions for different values of user distance from the camera. Average detection accuracy of the detector under controlled lighting conditions observed to be 96%, 92% and 88% for detection of eye blink, left wink and right wink, respectively. From the overall analysis it has been observed that the system gives significantly better performance under controlled lighting conditions than under natural lighting conditions, and when the user sits at a distance of about 0.5 meter from the camera.
Key-Words / Index Term
real-time eye blink detection, pixels’ motion analysis, varying lighting conditions, distance of user from camera, human-computer interaction
References
[1] E. Missimer and M. Betke, “Blink and Wink Detection for Mouse Pointer Control,” in PETRA’10 Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environments, 2010.
[2] T. Danisman, I. M. Bilasco, C. Djeraba, and N. Ihaddadene, “Drowsy Driver Detection System using Eye Blink Patterns,” in International Conference on Machine and Web Intelligence, 2010, pp. 230–233.
[3] E. Miluzzo, T. Wang, and A. T. Campbell, “EyePhone : Activating Mobile Phones With Your Eyes,” in Proceedings of the 2nd ACM SIGCOMM Workshop on Netwroking, Systems and Applications on Mobile Handhelds, 2010, pp. 15–20.
[4] K. Grauman, M. Betke, J. Lombardi, J. Gips, and G. R. Bradski, “Communication via Eye Blinks and Eyebrow Raises : Video-based Human-Computer Interfaces,” Universal Access in the Information Society, vol. 2, no. 4, pp. 359–373, 2003.
[5] M. Hashimoto, K. Takahashi, and M. Shimada, “Wheelchair Control Using an EOG- and EMG-Based Gesture Interface,” in IEEE/ASME International Conference on Advanced Intelligent Machatronics, 2009, pp. 1212–1217.
[6] S. S. Deepika and G. Murugesan, “A Novel Approach for Human Computer Interface on Eye Movements for Disabled People,” in 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT 2015), 2015.
[7] Y. Chen and W. S. Newman, “A Human-Robot Interface Based on Electrooculography,” in Proceedings of the 2004 IEEE International Conference on Robotics and Automation, 2004, pp. 243–248.
[8] T. Pallejà, E. Rubión, M. Tresanchez, and A. Fernández, “Using the Optical Flow to Implement a Relative Virtual Mouse Controlled by Head Movements,” Journal of Universal Computer Science, vol. 14, no. 19, pp. 3127–3141, 2008.
[9] T. Rajpathak, R. Kumar, and E. Schwartz, “Eye Detection Using Morphological and Color Image Processing,” in 2009 Florida Conference on Recent Advances in Robotics, FCRAR 2009, pp. 1–6.
[10] H. Drewes and A. Schmidt, “Interacting with the Computer using Gaze Gestures,” in INTERACT’07 Proceedings of the 11th IFIP TC 13 International Conference on Human Computer Interaction (Part-II), 2007, pp. 475–488.
[11] A. Krolak and P. Strumillo, “Eye-Blink Detection System for Human-Computer Interaction,” Universal Access in the Information Society, vol. 11, no. 4, pp. 409–419, 2012.
[12] P. Wang, M. B. Green, and Q. Ji, “Automatic Eye Detection and Its Validation,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
[13] A. A. Mohammed and S. A. Anwer, “Efficient Eye Blink Detection Method for Disabled- Helping Domain,” International Journal of Advanced Computer Science and Applications, vol. 5, no. 5, pp. 202–206, 2014.
Citation
Hari Singh, Jaswinder Singh, "Performance Analysis of Real-Time Eye Blink Detector for Varying Lighting Conditions and User Distance from the Camera," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.35-40, 2017.
An Evidential approach on Feature Subset Selection in Software Defect Prediction
Research Paper | Journal Paper
Vol.5 , Issue.12 , pp.41-49, Dec-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i12.4149
Abstract
In software quality research, software defect is a key topic. The characteristic of software attributes influences the performance and effectiveness of the defect prediction model. However this issue is not well explored to the best of our knowledge. So this paper focus on the problem of attribute selection in the context of software defect prediction, we propose a Dempster-Shafer Theory technique with modified combination rule known as Dubois And Prade’s Disjunctive Consensus Rule is adapted for selecting best set of attributes to improve the accuracy of the software defect prediction. Dempster-Shafer Theory (DST) offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The proposed method is evaluated using the data sets from NASA metric data repository.
Key-Words / Index Term
Software Defect, prediction, dempster shafer theory, probability, evidence, reliability
References
[1]. http://promise.site.uottawa.ca/SERepository
[2]. http://mdp.ivv.nasa.gov.
[3]. http://promise.site.uottawa.ca/SERepository/datasets-page.html
[4]. Y. Chen, P. Du,Xi , X.-H. Shen, “Research on Software Defect Prediction Based on Data Mining”, Computer and Automation Engineering(ICCAE), 2nd International Conference, (2010), vol. 1, pp. 563-567.
[5]. H.Najadat and I.Alsmadi, “Enhance Rule Based Detection for Software Fault Prone Modules”, International Journal of Software Engineering and Its Applications, vol. 6, no. 1, (2012).¬
[6]. A.Okutan, O. T.Yildiz, “Software defect prediction using Bayesian networks”, Empirical Software Engineering, (2014), vol. 19, no. 1, pp. 154-181.
[7]. T. Nu Phyu, “Survey of Classification Techniques in DataMining”, International MultiConference of Engineers and Computer Scientists, (2009); Hong Kong.
[8]. K. Sankar, S. Kannan and P.Jennifer, “Prediction of Code Fault Using Naive Bayes and SVM Classifiers Middle-East Journal of Scientific Research”, vol. 20, no. 1, (2014), pp.108-113.
[9]. Y. Ma,C. Bojan,“Singh:Robust prediction of fault-proneness by random forests ,Software Reliability Engineering”, ISSRE 2004. 15th International Symposium,(2004),pp. 417-428.
[10]. A. Chug1 and S. Dhall1, “Software Defect Prediction Using Supervised Learning Algorithm and Unsupervised Learning Algorithm”, The Next Generation Information Technology Summit (4th International Conference),(2013),pp.1-6.
[11]. S. Agarwal and D.Tomar, “A Feature Selection Based Model for Software Defect Prediction”, International Journal of Advanced Science and Technology, vol.65,(2014), pp. 39-58.
[12]. C.-P.Chang a,*, C.-P.Chu a, Y.-F.Yehb, “Integrating in-process software defect prediction with association mining to discover defect pattern”, Information and Software Technology ,vol. 51, no. 2, (2009), pp. 375-384.
[13]. M. L., H. Zhang, R. Wu, Z.-H. Zhou, “Sample-based software defect prediction with active and semi-supervised learning”, Automated Software Engineering , (2012), vol. 19, no. 2, pp. 201-230
[14]. P.Dhiman, M.C. Manish,“A Clustered Approach to Analyze the Software Quality Using Software Defects, Advanced Computing & Communication Technologies (ACCT)”, 2012 Second International Conference,(2012).
[15]. X. Yuan, H.W. Zhang, S. Ying,F. Wang, “Software defect prediction based on collaborative representation classification”, Proceedings in ICSE Companion 2014, 36th International Conference on Software Engineering, pp. 632-633.
[16]. K. Gao, T..M.Khoshgoftarr, “Software Defect Prediction for high- dimensional and class-imbalanced data”, 23rd International Conference on Software Engineering & Knowledge Engineering (SEKE`2011), Eden Roc Renaissance, (2011)Miami Beach, USA.
[17]. The Global Conference for Wikimedia,(2014); London.
[18]. M. Surendra Naidu, “Classification of Defects in Software Using Decision Tree Algorithm”, International Journal of Engineering Science and Technology (IJEST), (2013).
[19]. Black, Paul E. (2 February 2005). "greedy algorithm". Dictionary of Algorithms and Data Structures. U.S. National Institute of Standards and Technology (NIST). Retrieved 17 August 2012.
[20]. Dempster, A. P. (1967). “Upper and Lower Probabilities Induced by a MultivaluedMapping.” The Annals of Statistics 28: 325-339.
[21]. Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton, NJ, PrincetonUniversity Press
[22]. Klir, G. J. and M. J. Wierman (1998). Uncertainty-Based Information: Elements ofGeneralized Information Theory. Heidelberg, Physica-Verlag.
[23]. Zadeh, L. A. (1986). A Simple View of the Dempster-Shafer Theory of Evidence and itsImplication for the Rule of Combination. The AI Magazine. 7: 85-90.
[24]. Dubois, D. and H. Prade (1986). "A Set-Theoretic View on Belief Functions: LogicalOperations and Approximations by Fuzzy Sets." International Journal of GeneralSystems 12: 193-226.
[25]. Dubois, D. and H. Prade (1992). "On the combination of evidence in variousmathematical frameworks." Reliability Data Collection and Analysis. J. Flammand T. Luisi. Brussels, ECSC, EEC, EAFC: 213-241.
Citation
M. Jaikumar and V. Kathiresan, "An Evidential approach on Feature Subset Selection in Software Defect Prediction," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.41-49, 2017.
A New Approach of copy move Forgery Detection using Rigorous Preprocessing and Feature Extraction
Research Paper | Journal Paper
Vol.5 , Issue.12 , pp.50-56, Dec-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i12.5056
Abstract
these days, advanced pictures are being used in an extensive variety of uses and for numerous reasons. They additionally assume an imperative part in the capacity and exchange of visual data, particularly the mystery ones. With this far reaching utilization of advanced pictures, notwithstanding the expanding number of devices and programming of computerized pictures altering, it has turned out to be anything but difficult to control and change the real data of the picture. In this way, it has turned out to be important to check the credibility and the respectability of the picture by utilizing present day and advanced methods, which add to examination and comprehension of the pictures’ substance, and after that ensure their trustworthiness. There are many sorts of picture imitation, the most critical and prominent sort is called duplicate glue fabrication, which utilizes a similar picture during the time spent falsification. This sort of fraud is utilized for one of two things, first to cover a protest or scene by replicating the region of the picture and gluing it on another zone of a similar picture. In this paper we have presented a new approach of copy move forgery detection. proposed scheme uses Oriented FAST and rotated BRIEF(ORB) alternative of scale invariant feature transform (SIFT) technique which is integrated with modified local contrast modification-contrast limited adaptive histogram equalization(LCM-CLAHE). Experimental results shows that proposed scheme is more promising in terms of false positive rate(FPR) and true positive rate(TPR) compare to state of the art techniques.
Key-Words / Index Term
Image Processing, Image Enhancement, Histogram Equalization, SIFT, TPR, FPR, Copy move forgery, ORB
References
[1] J. M. Morel and G. Yu. ”On the consistency of the SIFT Method”. Lecture notes, August 11, 2008.
[2] D. G. Lowe. ”Distinctive image features from scale-invariant key-points”. International Journal of Computer Vision, 60, 2, pp. 91-110, 2004.
[3] A. C. Popescu and H. Farid. ”Exposing Digital Forgeries by Detecting Duplicated Image Regions”. 6211 Sudikoff Lab, Computer Science Department, Dartmouth College, Hanover, NH 03755 USA.
[4] J. Fridrich, D. Soukal, and J. Luk. ”Detection of Copy-Move Forgery in Digital Images”. Department of Electrical and Computer Engineering, b Department of Computer Science SUNY Binghamton, Binghamton, NY 13902-6000
[5] J. Yaduwanshi, and P. Bansal. ”A Novel Approach for Copy Move Forgery Detection Using Template Matching”. Proceedings of International Conference on Communication and Networks, Advances in Intelligent Systems and Computing 508, India,Springer Nature Singapore Pte Ltd., pp. 711-721, 2017, DOI 10.1007/978-981-10-2750-5 72.
[6] K. Sachdev, M. Kaur, and S. Gupta. ”A Robust and Fast Technique to Detect Copy Move Forgery in Digital Images Using SLIC Segmentation and SURF Keypoints”. Proceeding of International Con-ference on Intelligent Communication, Control and Devices, Advances in Intelligent Systems and Computing 479, Springer Science+Business Media Singapore, pp. 787-793, 2017, DOI 10.1007/978-981-10-1708-7 91.
[7] K. Hayat, and T. Qazi. ”Forgery detection in digital images via discrete avelet and discrete cosine transforms”. Computers and Electrical Engineering (2017) 111, 2017.
[8] L. D. Amiano, D. Cozzolino, G. Poggi, and L. Ver-doliva. ”A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization”. Cornell University Library,2017, arXiv:1703.04636v1 [cs.CV].
[9] M. Emam, Q. Han, Q. Li, H. Zhang, and M. Emam. ”A Robust Detection Algorithm for Image Copy-Move Forgery in Smooth Regions”. International Conference on Circuits, System and Simulation (ICCSS),London, UK, IEEE, 2017, DOI: 10.1109/CIRSYS-SIM.2017.8023194 .
[10] M. F. Mohamed Mursi, M. M. Salama, and Md. H. Habeb. ”An Improved SIFT-PCA-Based Copy-Move Image Forgery Detection Method”. International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) Volume 6, Issue 3, pp. 23-28, 2017.
[11] N. Kaur. ”A Review Paper on Copy Move Forgery Detection Techniques”. International Journal of Advanced Research in Computer Science,Volume 8, No. 7, pp. 157-161, 2017. DOI:10.26483/ijarcs.v8i7.4146.
[12] N. B. Abd. Warif, A. Wahid, Mohd. Y. I. Idris, R. Salleh,and F. Othman. ”SIFT-Symmetry: A Robust Detection Method for Copy-Move Forgery with Reflection Attack”. J. Vis. Commun. Image R., 2017, DOI: 10.1016/j.jvcir.2017.04.004.
[13] R. CRISTIN, and V. CYRIL RAJ. ”Consistency features and fuzzy-based segmentation for shadow and reflection detection in digital image forgery”. SCIENCE CHINA Information Sciences, Vol. 60 082101:1082101:18, 2017, DOI: 10.1007/s11432-016-0478-y.
[14] S. Mohan and M. Ravishankar. ”Modified Contrast Limited Adap-tive Histogram Equalization BAsed on Local Contrast Enhancement for Mammogram Image”. In: Das V.V., Chaba Y. (eds) Mobile Com-munication and Power Engineering. Communications in Computer and Information Science, vol 296. Springer, Berlin, Heidelberg , pp 397-403, 2013
[15] R. Dixit, and R. Naskar. ”Review, analysis and parameterisa-tion of techniques for copymove forgery detection in digital images”. IET Image Processing, 2017, DOI: 10.1049/iet-ipr.2016.0322.
[16] R. Dixit, R. Naskar, and Swati Mishra. ”Blur-invariant copy-move forgery detection technique with improved detection accuracy utilising SWT-SVD”. IET Image Processing, 2017, DOI: 10.1049/iet-ipr.2016.0537.
[17] S. Farooq, M. Haroon Yousaf, and F. Hussain. ”A generic passive image forgery detection scheme using local binary pattern with rich models”. Computers and Electrical Engineering 0 0 0 (2017), pp. 114, 2017, DOI: 10.1016/j.compeleceng.2017.05.008.
[18] S. Sadeghi, H. A. Jalab, K. Wong, D. Uliyan, and S. Dadkhah. ”KEYPOINT BASED AUTHENTICATION AND LOCALIZATION OF COPY-MOVE FORGERY IN DIGITAL IMAGE”. Malaysian Journal of Computer Science. Vol. 30(2), pp. 117-133, 2017.
[19] T. Mahmood, A. Irtaza, Z. Mehmood, and Md. T. Mahmood. ”Copy-move forgery detection through stationary wavelets and local binary pattern variance for forensic analysis in digital images”. Forensic Science International, DOI:10.1016/j.forsciint.2017.07.037.
[20] V. T. Manu, and B. M. Mehtre. ”Copy-move tampering detection using affine transformation property preservation on clustered keypoints”. SIViP, 2017, DOI 10.1007/s11760-017-1191-7.
[21] V. Thirunavukkarasu, J. S. Kumar, G. S. Chae, and J. Kishorkumar. ”Non-intrusive Forensic Detection Method Using DSWT with Reduced Feature Set for Copy-Move Image Tampering”. Wireless Pers Commun, 2017, DOI 10.1007/s11277-016-3941-1.
[22] X. Bi , and C. Pun. ”Fast Reflective Offset-Guided Searching Method for Copy-Move Forgery Detection”. Information Sciences, 2017, DOI: 10.1016/j.ins.2017.08.044.
[23] Y. Lai, T. Huang, and J. Lin H. Lu. ”An improved block-based matching algorithm of copy-move forgery detection”. Multimedia Tools Appl, 2017, DOI 10.1007/s11042-017-5094-y.
[24] Z. Fei, S. Wenchang, Q. Bo, and L. Bin. ”Image Forgery Detection Using Segmentation and Swarm Intelligent Algorithm”. Wuhan University Journal of Natural Sciences, Vol.22 No.2, pp. 141-148, 2017.
[25] D.Tralic , I. Zupancic , S. Grgic, and M. Grgic. CoMoFoD - New Database for Copy-Move Forgery Detection”. in Proc. 55th International Symposium ELMAR-2013, pp. 49-54, September 2013
[26] Dahale Sunil V, Thorat S.B., P.K. Butey, and M.P. Dhore. “Efficient Content Based Image Retrieval Using Fuzzy Approach”. International Journal of Computer Sciences and Engineering, Volume-5, Issue-10, pp. 38-43, 2017.
Citation
A.K. Chakraverti, V. Dhir, "A New Approach of copy move Forgery Detection using Rigorous Preprocessing and Feature Extraction," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.50-56, 2017.
A Survey on Blind Facial Image Enhancement Techniques
Survey Paper | Journal Paper
Vol.5 , Issue.12 , pp.57-63, Dec-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i12.5763
Abstract
Image Enhancement is one of the challenging issue in image processing. The objective of enhancement is modifying an image by removing the noise for making it easier to identify the key features. The current proficient strategy for recuperating dependable nearby arrangements of thick correspondences between two pictures with some common substance. The technique is intended for sets of pictures delineating comparable districts procured by various cameras and lenses, under non-inflexible changes, under various lighting, and over various foundations. Here use of another coarse-to-fine plan in which nearest-neighbor field calculations utilizing Generalized Patch Match are interleaved with fitting a worldwide non-direct parametric shading model and amassing reliable coordinating districts utilizing locally versatile imperatives. Contrasted with past correspondence approaches, technique joins the better of two universes: It is thick, as optical stream and stereo reproduction strategies, and it is likewise powerful to geometric and photometric varieties, sparse feature matching. This shows the convenience of technique utilizing three applications for programmed case based photo improvement: altering the tonal attributes of a source picture to coordinate a reference, exchanging a known mask to a new image, and kernel, and portion estimation for picture deblurring. The present work investigated various image enhancement techniques for noise elimination and to identify the key features of the image.
Key-Words / Index Term
Correspondence, color transfer, Patch Match, nearest neighbor field, deblurring
References
[1]. An, X., And Pellacini, F. 2010. User-Controllable Color Transfer. Computer Graphics Forum 29, 2, 263–271.
[2]. Barnes, C., Shechtman, E., Goldman, D. B., And Finkelstein, A. 2010. The Generalized Patchmatch Correspondence Algorithm. In Proc. Eccv, Vol. 3, 29–43.
[3]. Lucas, B. D., And Kanade, T. 1981. An Iterative Image Registration Technique with An Application to Stereo Vision. In Proc. Darpa Image Understanding Workshop, 121–130.
[4]. Snavely, N., Seitz, S. M., And Szeliski, R. 2006. Photo Tourism: Exploring Photo Collections In 3d. Acm Trans. Graph. 25 (July), 835–846.
[5]. Liu, C., Yuen, J., Torralba, A., Sivic, J., And Freeman, W. T. 2008. Sift Flow: Dense Correspondence Across Different Scenes. In Proc. Eccv, Vol. 3, 28–42
[6]. Cho, M., Shin, Y. M., And Lee, K. M. 2008. Co-Recognition of Image Pairs by Data-Driven Monte Carlo Image Exploration. In Proc. Eccv 2008, Vol. 4, 144–157.
[7]. Rother, C., Minka, T. P., Blake, A., And Kolmogorov, V. 2006. Cosegmentation Of Image Pairs by Histogram Matching– Incorporating A Global Constraint into Mrfs. In Proc. Cvpr 2006, Vol. 1, 993–1000.
[8]. Ancuti, C., Ancuti, C. O., And Bekaert, P. 2008. Deblurring By Matching. Computer Graphics Forum 28, 2, 619–628.
[9]. [9] Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., And Gool, L. V. 2005. A Comparison of Affine Region Detectors. Int. J. Comput. Vision 65 (November), 43–72.
[10]. Bai, X., Wang, J., Simons, D., And Sapiro, G. 2009. Video Snapcut: Robust Video Object Cutout Using Localized Classifiers. Acm Trans. Graph. 28, 3 (July), 70:1–70:11.
[11]. Eisemann, M., Eisemann, E., Seidel, H.-P., And Magnor, M. 2010. Photo Zoom: High Resolution from Unordered Image Collections. In Proc. Graphics Interface, 71–78.
[12]. Using Photographs to Enhance Videos Of A Static Scene. In Rendering Techniques 2007, Eurographics, 327–338.
[13]. Cho, S., And Lee, S. 2009. Fast Motion Deblurring. Acm Trans. Graph. 28, 5 (December), 145:1–145:8.
[14]. Bhat, P., Zitnick, C. L., Snavely, N., Agarwala, A., Agrawala, M., Curless, B., Cohen, M., And Kang, S. B. 2007.
[15]. Eisemann, E., And Durand, F. 2004. Flash Photography Enhancement Via Intrinsic Relighting. Acm Trans. Graph. 23 (August), 673–678.
[16]. Reinhard, E., Ashikhmin, M., Gooch, B., And Shirley, P. 2001. Color Transfer Between Images. Ieee Comput. Graph. Appl. (September 2001).
[17]. Brox, T., Bregler, C., And Malik, J. 2009. Large Displacement Optical Flow. In Proc. Cvpr 2009, Ieee, 41–48.
[18]. Cho, M., Lee, J., And Lee, K. 2009. Feature Correspondence and Deformable Object Matching Via Agglomerative Correspondence Clustering. In Proc. Iccv, 1280–1287.
[19]. Dale, K., Johnson, M. K., Sunkavalli, K., Matusik, W., And Pfister, H. 2009. Image Restoration Using Online Photo Collections. In Proc. Iccv, Ieee.
[20]. Liu, X., Wan, L., Qu, Y., Wong, T.-T., Lin, S., Leung, C.-S., And Heng, P.-A. 2008. Intrinsic Colorization. Acm Trans. Graph. 27, 5 (December), 152:1–152:9.
[21]. Joshi, N., Matusik, W., Adelson, E. H., And Kriegman, D. J. 2010. Personal Photo Enhancement Using Example Images. Acm Trans. Graph. 29, 2 (April), 12:1–12:15.
[22]. Zelnik-Manor, L., And Irani, M. 2006. On Single-Sequence and Multi-Sequence Factorizations. Int. J. Comput. Vision 67 (May), 313–326.
Citation
K. Sahithi, G. Karuna, "A Survey on Blind Facial Image Enhancement Techniques," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.57-63, 2017.
Analysis of Customer Behaviour using Modern Data Mining Techniques
Research Paper | Journal Paper
Vol.5 , Issue.12 , pp.64-66, Dec-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i12.6466
Abstract
As we know enormous amount of data is present on the Internet and in order to get value out of this data and present the information to the user in a very simple form, researchers are working hard to collate this data.This colossal size of data on Internet is the most important source for decision making and marketing now-a-days. The paper presents a proposed model to understand online customer’s buying behaviour based on decision tree and artificial neural network models.Our model is comparatively good at predicting the precision of customer’s buying behaviour.
Key-Words / Index Term
Artificial Neural Network ,Buying Behaviour, Confusion Matrix, Data Mining, Decision Tree
References
[1] T. SH. Teo,“To buy or not to buy online: adopters and non-adopters of online shopping in Singapore”, Behavior and Information Technology, Vol. 25(6) , pp.497-509,2006.
[2] S. L. Ansari, “An integrated approach to control system design”, Accouting, Organizations and Society, Science Direct, Vol2 (2), pp. 101-112 , 1977.
[3] S. Qing, D. Gollmann, J. Zhou,“Information and communications society”, 5th International Conference, ICICS Huhehaote, China, October, pp. 10-13, 2003.
[4] S. Vallamkondu and L. Gruenwald, “Integrating purchase patterns and traversal patterns to predict HTTP requests in E-Commerce sites”, IEEE International Conference on E-Commerce, USA, pp. 256 – 263,2003.
[5] Y-S. Lee, S-J. Yen, G-H. Tu, M-C. Hsieh, “Mining travelling and purchasing behaviors of customers in electronic commerce environment”, Proceedings of the IEEE International Conference on e-Technology, e-Commerce and e-Service,2004.
[6] K. D. Satokar , S. Z. Gawali, )“Web search result personalizationusing web mining”, International Journal of Computer Applications (0975 – 8887) Vol. 2 (5), 2010.
[7] Kiruthika , R. Jadhav, D. Dixit, Rashmi, A. Nehete, T. Khodkar, T, “Pattern Discovery Using Association Rules”, International Journal of Advanced Computer Science and Applications, Vol 2 (12), pp.69-74, 2011.
[8] A. Todi, A. Agrawal, A. Taparia, N. Lakhmani, R. Shettar,“Classification Of E-Commerce Data Using Data Mining”,International Journal Of Engineering Science & Advanced Technology, Vol.2, (3), pp.550 – 554,2012.
Citation
S.J. Nasti, M. Asgar, M.A. Butt , "Analysis of Customer Behaviour using Modern Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.64-66, 2017.